Clinical epidemiology - presentation. Successes of modern natural science Characteristics of certain types of empirical research. Analytical methods of research. cohort study

CLINICAL EPIDEMIOLOGY.
EPIDEMIOLOGICAL METHODS
STUDIES AND THEIR CHARACTERISTICS
Associate Professor KOKTYSHEV I.V.

Lecture plan:

Lecture plan:
1. Epidemiology as a science
2. Methods of epidemiological research
3. Characteristics of individual types of research
3.1 Empirical research methods
3.2 Experimental research methods
4. Continuous and selective methods. Ways
sampling.

Epidemiology (traditional presentation)

is a system of scientific knowledge,
justifying skid warning
infections, infectious
diseases among the population, and in case of their
occurrence - elimination of epidemic
foci, a decrease in the overall level
infectious disease.

Up to this day
preserved archaic
understanding of epidemiology
as a science that
only studies
epidemic process and is associated
exclusively with the study
infectious diseases
and methods of dealing with them

Epidemiology (modern presentation)

is the science that studies the prevalence
health conditions or events,
determinants of these states and events, in
specially defined populations for
management and control of health problems.
That is, now all over the world epidemiology
regarded as a science that studies
distribution patterns and methods
study of any disease.
It is now considered a basic science
on public health and is now often referred to as
CLINICAL EPIDEMIOLOGY.

The most important aspects of clinical epidemiology

1) It is a science that is based on the theory of probability,
statistics and methods of research analysis;
2) This is a method of causal argumentation that allows
in practice to prove or disprove the put forward
hypotheses about the causes
prevention and treatment of diseases (outcomes);
3) It is a practical tool that
allows, on a scientific basis, to preserve, strengthen,
maintain the health of the population.

Objectives of epidemiology
describe the incidence of the population (descriptive or
descriptive purpose);
explain the incidence, i.e. identify the causes
occurrence and spread of disease
(analytical goal);
make a prospective forecast of morbidity
population (prognostic target);
develop a concept for the prevention of individual
groups of diseases (prophylactic goal);
assess potential effectiveness
traditional and new prevention or treatment measures
diseases (as the basis of evidence-based medicine).

The first goal is to describe the incidence of the population
any disease, it means to identify features
dynamics and structure of morbidity, taking into account
time
places
occurrence
disease
and
individual
or
group
characteristics
sick.
To describe means to present a comparative
morbidity characteristics, i.e. reveal not
simply “what they get sick”, and “what they get sick more often, and what
less often”, not just “when they get sick”, but “when they get sick
more often, and when less often”, not just “where they get sick”, but “on
which territory they get sick more often, and in which less often,
just “who gets sick”, but “what population groups get sick
more often and which less often.

The second goal is to identify the causes of the occurrence
individual disease means to answer the question
beginning with the word "why". For example,
“Why do people get sick more often at some times, and at other
some less often”, “why, in some groups
population, the incidence rate is higher than in
others”, etc.
Basic
way
identifying
reasons
disease occurrence is based on comparative
studying the frequency of diseases in different groups
population with a specific spectrum and
intensity of biological, social or
natural factors (risk factors).

Features of the epidemiology of noncommunicable diseases

usually a latency period for noncommunicable diseases
significantly longer than infectious, and a specific period
its unpredictable;
chronic disease develops gradually and its symptoms in
of the examined persons vary in a wide range, which increases
the likelihood of misdiagnosis;
non-communicable diseases are characterized by a multifactorial nature
etiology and pathogenesis, and the clearly dominant factor is often
missing;
unlike infectious epidemiology, it is impossible to isolate
non-receptive part of the population and determine whether there is
the absolute resistance of a particular person to a particular
chronic non-communicable disease;
forecasts of morbidity and the effectiveness of preventive
events are probabilistic in nature and are justified by
relation to the population as a whole.

Challenges facing the epidemiology of noncommunicable diseases

Study of prevalence and natural course
certain diseases by population group,
identifying the extent of the problems associated with these
diseases.
Determination of factors of external and internal environment,
that help or hinder
occurrence and spread of these diseases.
Identification of priority security issues
population health.
Development of measures to eliminate or
the maximum possible weakening of the effect
unfavorable factors. Efficiency study
preventive and curative measures.

Methods of epidemiological (clinical) studies

- These are, first of all, all those methods that
study the patterns of occurrence and
spread of infectious and
non-communicable diseases among the population,
based on the application of statistical
indicators and values.
"An epidemiologist is a doctor who can count."
Statistical literacy, i.e. body of knowledge
and skills that form the basis of diagnostic
technique in clinical epidemiology is
compulsory
element
"epidemiological
physician culture.

Classification of clinical research methods

Clinical researches
empirical
descriptive
Analytical
1. Case report 1. Case control
2. Series of cases
2. Cohort
3. One-time
(transverse)
experimental
controlled
Randomized
Uncontrolled
Non-randomized
1. Open research
2. Closed studies:
a) simple blind
b) double blind
c) triple blind
3. Multicenter

Methods of epidemiological research

Depending on the purpose, epidemiological
research is divided into exploratory
(putting forward a hypothesis) and testing the hypothesis.
By the nature of interventions - on empirical
(observational) or experimental.
In terms of duration of observation
for the health status of the studied contingent
epidemiological studies can be
one-time or long-term
(longitudinal), which are divided into
prospective and retrospective.
depending on the collection method
studies can be: continuous or
selective.

Prospective studies are studies
in which data is accumulated after
how it was decided to conduct the study.
Retrospective studies -
studies in which the data
accumulate before the study
(copying data from the medical
documentation in the archives).

Empirical Research

is research without intentional
interference with the natural course and
the development of the disease.
Empirical research is divided into
descriptive and analytical.
Descriptive studies include:
case description method and series description method
cases.
Analytical methods include methods
cohort studies and studies
"case-control".

Experimental studies

- studies in which
purposeful and conscious control
the main parameters that are the subject
study, as well as the distribution of objects
research (sick and healthy individuals) on
certain groups.
Experimental studies
subdivided into field and clinical,
controlled and uncontrolled,
randomized and non-randomized.

Descriptive epidemiology is the study of:
frequency and spread of diseases (outcomes)
in a specific area (country, region,
district, city, village), at a certain time (month,
year, 5 years, etc.), in different population groups
(differentiated by sex, age,
nationality, socio-economic
position, education, profession, etc.);
the course of diseases;
effectiveness of diagnostic criteria;
prevalence of potentially dangerous
factors.

Characteristics of certain types of empirical research. Descriptive research methods

Description of individual cases - the oldest
method of medical research. Wherein
details the data obtained through
observation of one or more cases
diseases (no more than 10 patients).
This method allows to draw the attention of physicians to
new or little-known diseases, manifestations
or combinations of diseases. Used to describe
unusual manifestations of diseases and presents
is the only way to report a rare
clinical event, risk, prognosis or treatment.
Of interest only at the initial stage
study of medical intervention.

Characteristics of certain types of empirical research. Descriptive research methods

Case series description - study,
including usually descriptive statistics
diseases (number of a group with a certain
disease - 10 or slightly more patients).
This is an "open" study without a group
comparison (control).
A series of cases is the most common
way to describe the clinical picture of the disease.
Allows you to create an impression
the effectiveness of the intervention, but does not confirm
her.

Characteristics of certain types of empirical
research. Descriptive research methods
Cross-sectional studies (one-shot) ‒
studies looking at the prevalence
diseases (conditions) in a population at a certain moment
time.
Answer the question "How much?". Used for
studying disease prevalence or outcome,
studying the course of the disease, the staging of the process.
Studies in which each patient is examined
once. Purely cross-sectional studies occur
rarely. The best option is a random sample. Often for
questionnaires are used to conduct the study.

Characteristics of certain types of empirical research. Descriptive research methods. Cross-sectional studies

Examples of issues to be resolved
(scientific and practical problems)
What is the prevalence of dementia in
older populations?
What is the prevalence of anemia in
country?
What is the frequency of complications of operations
appendectomy?
What is the "normal" height of a 3 year old?
Is it true that half of all cases of sugar
diabetes remains undiagnosed?

Cross-sectional studies

BENEFITS
Economical;
Takes little time;
The first step in looking for risk factors and formulating a hypothesis;
Quick results;
The best design for studying the status quo of diseases and
states.
LIMITATIONS
They do not give a correct idea of the causes of diseases;
Imply only fleeting conditions and diseases;
Lack of temporary connection;
Those patients who died and recovered are not taken into account;
May not be representative of the entire population;
Systematic errors due to the inclusion of "old"
cases.

Characteristics of certain types of empirical research. Analytical methods of research.

Analytical epidemiological
research is used to
establish causal relationships between
diseases and other factors
risk (professional, social, environmental,
genetic, etc.), as well as to assess
effectiveness of preventive and
medical interventions.

Cases are identified first (selection of patients with
disease being studied)
Retrospectively put forward a hypothesis about a possible factor
risk
Select a control group of people who do not have this
disease, similar in other respects to the studied
group
Determine the presence or absence of a risk factor in these
two groups.
Most suitable for studying rare events, as well as
if you need fast results
research
Best suited for answering the etiology question
Example: Causes of congenital hydrocephalus in children

Characteristics of certain types of empirical research. Analytical methods of research. Case-control.

Research weaknesses
The retrospective nature does not allow
accurately record temporary
relationships between phenomena
Possible errors in the assessment
validity of impact
"Artificial" selection of comparison groups

Cohort - a group of people, initially united by some
a common feature (for example, healthy or people on
a certain stage of the disease)
Prospectively follow the outcome in individuals exposed to
exposure to a risk factor
The best type of clinical research for when
experiment is impossible
Outcomes not yet known at study entry
In the process of continuous observation, it is noted in which proportion
observed developed disease (or other outcome)
Example: Does the whooping cough vaccine cause brain damage?

Characteristics of certain types of empirical research. Analytical methods of research. cohort study.

It often takes years to evaluate outcomes
observations
More expensive than
case-control studies
The main disadvantage of cohort studies is
to study rare outcomes requires
observation of large groups during
long time

Characteristics of certain types of experimental studies. Uncontrolled non-randomized.

Examining anamnestic data
patients
Groups are made up by a doctor
flaw
Complete subjectivity of assessments and conclusions
Groups are not fully comparable
Comparison between groups is unreliable

Characteristics of experimental studies.
Controlled randomized.
Randomization ("random" - "random") - a procedure,
providing a random distribution of patients in
experimental and control groups.
The purpose of the randomized clinical trial is to evaluate
specific ("biological") therapeutic effect of the intervention.
Randomization ensures that there are no differences between groups.
Ensures that the allocation of patients to groups has been
random and not influenced by subjectivity
researchers, nor systematic error.
Provides a basis for performing statistical analysis on
quantitative evaluation of data related to the therapeutic effect.
In combination with the blind method, it helps to avoid systematic
errors associated with the selection of patients and the appointment of treatment,
due to the predictability of the distribution of patients.

Characteristics of experimental studies. Controlled randomized.

Basic requirements for such research
The groups are the same, that is, they are comparable in terms of the main
featured from the start
The management of patients in groups is the same except for
intervention
All cases must be analyzed without
exceptions included in the study
Types of control
placebo control
Control with no treatment
Parallel control of different drugs
Parallel control of different doses of the same drug

Characteristics of experimental studies. Controlled randomized.

Simple blind
the patient does not know what medicine he is taking
double blind
neither the doctor nor the patient knows what medicine
the patient receives
carried out in accordance with the protocol, under the control
ethical committee
triple blind
when neither the patient, nor the doctor, nor the specialist,
processing the results, do not know what
treatment, experimental or control,
receives one or another patient
Multicenter
allows faster patient recruitment and faster
complete research
results apply to wider region

Characteristics of experimental studies. Controlled randomized.

Advantages and disadvantages of RCTs
Advantages
The most convincing way to prove a hypothesis
Control known and unknown distorters
factors
Possibility of subsequent meta-analysis
Flaws
High price
The execution method is complicated
Ethical Issues

Summary

There is no better study design
exists
For each question there are different
designs - different ways
research
Every design has its weaknesses and
strengths
The only thing that really matters
research quality

In the real world, a complex process takes place
- the process of emergence and distribution
diseases. It is this process that is
object of study for most medical
sciences in order to understand the causes of this phenomenon.
Causes show their pathological influence on
different levels of life organization: suborganismal
(tissue, cell, molecule), organismal (organs
human) and supraorganismal (the level of society,
population populations).
Therefore, the main subject of study in
epidemiology is a pathology that manifests itself
on the
supraorganismal
level,
then
there is
morbidity of the population.

Although personal experience and knowledge of the mechanisms of disease development is certainly important, the following should be taken into account:

In most cases, the diagnosis, prognosis and results of treatment for a particular patient are not clearly defined and therefore must be expressed in terms of probabilities; these probabilities for a particular patient are best estimated on the basis of previous experience gained in relation to groups of similar patients; since clinical observations are carried out on patients who are free in their behavior, and physicians with different qualifications and their own opinion make these observations, the results may be subject to systematic errors leading to incorrect conclusions; any observations, including clinical ones, are subject to the influence of chance; to avoid misleading conclusions, physicians must rely on studies based on rigorous scientific principles, using methods to minimize bias and account for random errors.

Social aspect of clinical epidemiology

Influential forces in modern society have accelerated the recognition of the methods and possibilities of clinical epidemiology. The cost of medical care has reached such a level that even the richest groups of the population are not able to pay for all the desired types of services. It has been shown that the use of new clinical methods is not necessarily accompanied by corresponding changes in clinical outcomes; consequently, far from all conventional or expensive types of treatment are useful for the patient. Methods are now being developed to better evaluate clinical data that health care leaders can use. There was a consensus that health care should be based on the results of rigorous research itself and judged by the results, taking into account the financial costs that society can afford. In addition, individual patients are increasingly considered as part of large groups of similar patients; this helps not only to make more accurate individual predictions, but also to choose the most appropriate way to use limited medical resources for optimal care for as many people as possible.

Basic principles

The main goal of clinical epidemiology is to implement methods of clinical observation and data analysis that ensure correct decision making. Most credible answers to clinical questions are based on the principles described in this section.

Clinical Issues

The main questions that clinical epidemiology poses are listed in Table. 1.1. These are the same questions that the patient and doctor had in the example given at the beginning of the chapter. These are the most commonly discussed between doctors and patients.

Table 1.1 Clinical questions

Subject of discussion	Question
Deviation from the norm	Is the patient healthy or sick?
Diagnosis	How accurate are the methods used to diagnose the disease?
Frequency	How common is this disease?
Risk	What factors are associated with an increased risk of the disease?
Forecast	What are the consequences of the disease?
Treatment	How will the course of the disease change with treatment?
Prevention	Are there measures to prevent disease in healthy people? Does the course of the disease improve with early recognition and treatment?
Cause	What factors lead to the disease? What are its pathogenic mechanisms?
Price	How much does the treatment of this disease cost?

Clinical Outcomes

The clinical events of interest to clinical epidemiology are primarily the outcomes that matter most to patients and health care workers (Table 1.2).

Table 1.2 Outcomes of the disease (in English - five "D")*

* The sixth "D" - financial difficulties (Destitution) can also be added to this list, since an important consequence of the disease is the cost of funds (for the patient himself or for society).

**Unhealthy, subjective perception of illness.

It is these phenomena that doctors try to understand, predict, interpret and change in the treatment of patients. An important difference between clinical epidemiology and other medical sciences is that all phenomena are studied directly on humans, and not on animals or elements of the human body, such as tissue cultures, cell membranes, chemical mediators, genetic nucleic acid sequences. Biological events cannot be considered equivalent to clinical outcomes until there is direct evidence of their relationship. In table. 1.3 presents some of the biological phenomena and clinical outcomes in the treatment of patients with HIV infection. Our knowledge of the pathogenesis of HIV infection suggests that clinical outcomes such as opportunistic infections, Kaposi's sarcoma, and death could be improved by interventions that prevent lymphocyte depletion. CD4+ and reduce the level of antigen p24. However, there is evidence that these markers do not provide a complete picture of disease progression and response to treatment. It is naive to assume that the effect of an intervention on the outcome of a disease can be due solely to the effect on physiological parameters, since the final result is determined by many other factors. Therefore, clinical decisions should be based on direct evidence of improved clinical outcomes per se.

Table 1.3 Biological events and clinical outcomes: Treatment of HIV-infected patients

Quantitative approach

Clinical science is especially compelling when it can provide a quantitative approach to measurement. This is partly due to the fact that quantitative results provide more reliable evidence, allow error to be assessed, and facilitate the exchange of information both between doctors and between doctor and patient. Clinical outcomes such as death, disease, or disability can be quantified. Although qualitative observations in clinical medicine are also important, they are not the focus of clinical epidemiology. It is rarely possible to accurately predict a particular clinical outcome. Rather, based on the results of the study, it is possible to determine the likelihood of a particular outcome. The clinical epidemiological approach accepts that clinical prognosis is uncertain, but can be quantified in terms of probabilities: for example, symptoms of coronary heart disease occur in 1 in 100 middle-aged men per year; smoking doubles the risk of death at any age; taking estrogens reduces the risk of fractures caused by osteoporosis by 2 times.

Populations and samples

Basically, population(population) is a large group of people living in a particular geographic region (for example, in North Carolina) or having some trait (for example, over 65 years old). A population may simply be a subset of the population (typically populations in epidemiological studies of the causes of disease). It may consist of patients admitted to a particular clinic or patients with a particular disease (which is more common in clinical trials). Thus, one can speak of a general population, a hospital population, or a population of patients with a specific disease. Sample(sample) is the part of the population obtained by selection. Clinical studies are usually performed on samples. For practical reasons, the estimation of the characteristics of a population has to be done by estimating these characteristics from a sample.

systematic error

Systematic error, or bias(bias) is "a systematic (non-random, unidirectional) deviation of the results from the true values" . Suppose drug A was found to work better than drug B. What kind of bias could lead to this conclusion if it turned out to be wrong? Drug A could be given to patients with lesser disease severity; then the results will be due not to the different effectiveness of drugs, but to the systematic difference in the condition of patients in the two groups. Or drug A tastes better than drug B, so patients adhere to the treatment regimen more strictly. Or drug A is a new, very popular drug, and B is an old drug, so researchers and patients tend to think that a new drug will definitely work better. These are examples of possible systematic errors. Patient follow-up (whether in treatment or research) is particularly susceptible to bias due to simple negligence. While participating in the study, patients often continue to behave as they want, which sometimes does not meet the conditions for obtaining rigorous scientific results. When they try to conduct an experiment like a laboratory experiment with them, nothing often comes of it. Some patients refuse to participate, others drop out during the course of the study or choose to change their treatment. Moreover, some of the most important characteristics from a human point of view - emotions, comfort, behavior - are much more difficult to measure than physical parameters such as blood pressure or serum sodium. In addition, clinicians themselves tend to believe in the success of their treatment (most patients would not want to be treated by a doctor who thinks otherwise). Because of this attitude, which is so important in medical practice, clinical observations are particularly prone to bias. Although there are dozens of varieties of systematic errors, most of them can be classified into one of three main categories (Table 1.4).

Table 1.4 Bias in clinical observation

, occurs when the compared groups of patients differ not only in the trait being studied, but also in other factors that affect the outcome.

, occurs when different methods of measurement are used in the compared groups of patients.

, occurs when one factor is related to another, and the effect of one distorts the effect of the other.

Selection bias(selection bias) occurs when the compared groups of patients differ not only in the main characteristics studied, but also in other factors that affect the result of the study. Groups of patients often differ in many ways - age, gender, disease severity, comorbidities, intervention methods. If we compare data for two groups that differ not only in specific factors of interest to us (for example, the method of treatment or the alleged cause of the disease), but also in other ways, on which the outcome also depends, then the result of the comparison will turn out to be biased and will not allow us to draw conclusions. about the degree of influence of the factor of interest to us. In the example above, this error would occur if patients treated with drug A had a less severe disease than those treated with drug B. Measurement bias(measurement bias), occurs when different methods of assessment are used in the compared groups of patients. Such an error could result from the use of information taken from medical records to study the risk of thromboembolism in women in connection with oral contraceptives. Suppose that we compared the frequency of oral contraceptive use in two groups of women hospitalized for phlebothrombosis and for other reasons. It is easy to assume that women with phlebothrombosis who have heard about the possible effect of estrogens on the development of thrombosis are more likely to be mentioned taking these drugs than women who do not suffer from this disease. For the same reasons, doctors will ask more about the use of oral contraceptives specifically in women with phlebothrombosis. Under such circumstances, the relationship between the use of oral contraceptives and the development of phlebothrombosis can be identified precisely because of the approach to collecting information, and not at all because such a relationship exists in reality. Bias due to confounding factors(confounding bias), occurs when two factors are interconnected ("go in pairs"), and one of them distorts the effect of the other. This may be due to selection bias, chance, or a real relationship between factors.

Example. Is an infection caused by the herpes virus a cause of cervical cancer? It is firmly established that the prevalence of herpes virus infection is higher among women with cervical cancer than among women without the disease. However, herpes and other infections that can also cause cervical cancer are sexually transmitted. In particular, it has been proven that cervical cancer is caused by the human papillomavirus. It is possible that the high prevalence of herpes virus infection among patients with cervical cancer is only indirectly related to the true cause, also sexually transmitted, and is a consequence of higher sexual activity (Fig. 1.1). To show that the herpes virus causes cervical cancer independently of other factors, it is necessary to determine the effect of the herpes virus in the absence of other factors associated with increased sexual activity.

Systematic errors arising from selection and exposure to confounding factors are not mutually exclusive. However, they are considered separately because they refer to different stages of clinical observation or research. Selection bias occurs in the selection of patient groups for observation, therefore, this danger should be kept in mind during study design. Error due to confounding factors should be taken into account in the data analysis process after the end of the study. Often several types of bias are found in the same study, as shown in the following hypothetical example.

In this example, selection bias could occur if those who chose to participate in the program had a lower risk of developing CAD at baseline, for example due to low serum lipids or a family history of CAD that was not burdened. A systematic error in the measurement could have appeared due to the fact that volunteers who were regularly examined had a higher chance of detecting coronary artery disease. Finally, a reduction in the risk of developing coronary artery disease under the influence of physical training could be drawn due to a bias due to confounding factors: the volunteers who participated in the physical training program were less likely to smoke, and smoking is known to be a risk factor for the development of coronary artery disease. By itself, the possibility of bias does not mean that it is necessarily present in a particular study. In order for both researchers and readers to successfully deal with systematic errors, it is necessary first of all to know where and how to look for them, and what can be done to level their influence. In addition, it is necessary to be able to determine whether a systematic error does occur and whether it is large enough to affect the outcome of the study in a clinically significant way.

Random error

Diseases are usually studied in samples of patients, rather than in the general population (general population) of all individuals with the condition in question. The results of observations in a sample, even if the sample is unbiased, may not reflect the position in the population as a whole due to random error. However, if observations are repeated in many samples of such patients, then the results obtained will fluctuate around the true value. The deviation of the result of an (individual) observation in the sample from the true value in the population, due solely to chance, is called random variation. We are all familiar with randomness when a coin tossed 100 times does not land on heads exactly 50 times. A similar phenomenon of random variation applies to the example discussed, evaluating the efficacy of drugs A and B. Suppose that in a study evaluating two treatments, all possible biases are eliminated. Let us also assume that the two drugs are in fact equally effective, and that each of them produces an improvement in about half of the patients. However, in a single study with a small number of patients in the compared groups, it may well be (purely by chance) that drug A improves in a greater percentage of cases than drug B, or vice versa. Random error can interfere at any stage of clinical observation. In the comparative evaluation of preparations A and B, random variations occur in the selection of patients, the formation of treatment groups, and the measurements in groups. Unlike systematic error, which causes the estimate to deviate from the truth either in one direction or the other, random variation with the same probability leads to overestimation and underestimation. As a result, the average of the results of many unbiased observations in the samples tends to the true value in the population, even if the results obtained in individual small samples are far from it. In the analysis of clinical data, the probability of random variations is determined by statistical methods. The use of statistics also helps to minimize random error by choosing the best methods for research and data analysis. However, random variation can never be completely eliminated and must be taken into account when evaluating the results of clinical observations. The relationship between systematic and random error is illustrated by the example of measurements of diastolic blood pressure (BP) in one patient (Fig. 1.2).

The true value of diastolic blood pressure obtained with the introduction of an intra-arterial cannula in this patient was 80 mm Hg. However, this method is not applicable as a routine method, and in clinical practice, blood pressure is usually measured indirectly using a sphygmomanometer. This simpler tool makes mistakes - deviations from the true values. The error lies in the fact that all the readings of the sphygmomanometer in this case are shifted to the right of the true value (see Fig. 1.2). The deviation of the sphygmomanometer readings to the right (systematic error) can be due to various reasons: a poorly calibrated device, an inappropriate cuff size, or impaired hearing by the doctor. The shift may also depend on the choice of tone, which is used to determine diastolic blood pressure. Usually these are phases IV and V Korotkoff sounds, which tend to disappear slightly above and below the true level of diastolic pressure, respectively, and in obese individuals, the relationship between Korotkoff sounds and blood pressure is generally unpredictable. In addition, individual sphygmomanometer readings are subject to errors due to random variations, which is reflected in the spread of sphygmomanometer readings around the mean value (90 mmHg). The two sources of error, bias and randomness, are not mutually exclusive. As a rule, they are present at the same time. They must be distinguished, since one and the other have to be dealt with in different ways. Theoretically, bias can be prevented by correct clinical observations or by correction in subsequent data analysis. An attentive reader will easily detect a systematic error, if any. Much of this book is about how to recognize, avoid, or minimize bias. Unlike bias, the effect of chance cannot be eliminated, but can be reduced by a well-designed study, and the remaining error can then be estimated statistically. Similarly, the influence of known systematic errors can be eliminated. However, no data processing is able to correct the unknown systematic error. Some experts object in principle to statistical processing of data subject to bias due to poorly designed research, since this gives nothing but a false impression of the scientism of work that is not credible.

For independent extracurricular work

to practical lesson No. 2

in the discipline Evidence-based medicine

specialty (direction of training)

"Medicine"

Compiled by: cand. honey. Sciences Babenko L.G.

Theme II. Clinical epidemiology is the basis of evidence-based medicine

Purpose of the lesson: study of goals, objectives, principles and methodology of evidence-based medicine; criteria and degree of evidence for studies of etiology, diagnosis, treatment and prognosis and the scope of their application; historical aspects of its formation and development.

Tasks:

1. To acquaint students with the sections of evidence-based medicine, its goals, objectives, principles, components, aspects and methodology, its place among other medical sciences.

2. Describe the degree of evidence in clinical studies of etiology, diagnosis, treatment and prognosis and the scope of its application.

3. Highlight the historical aspects of the creation, formation and development of evidence-based medicine

4. Familiarize students with the organization that professes the methodology of evidence-based medicine Cochrane Collaboration, its goals, objectives and principles.

5. Describe the difficulties of introducing evidence-based medical practice and ways to overcome them in domestic medicine.

The student must know:

1 - before studying the topic (basic knowledge):

The main factors, trends in the development of biomedical sciences and the needs of practical medicine in modern conditions;

Components of building a medical view on methodological approaches to conducting clinical trials, evaluating and applying their results;

Mathematical methods for solving intellectual problems and their application in medicine;

Fundamentals of medical history;

Theoretical foundations of computer science, collection, storage, search, processing, transformation of information in medical and biological systems, the use of information computer systems in medicine and healthcare;

The concepts of etiology, pathogenesis, morphogenesis, pathomorphosis of the disease, nosology, the basic concepts of general nosology:

Functional bases of diseases and pathological processes, causes, main mechanisms of development and outcomes of typical pathological processes, dysfunctions of organs and systems.

2 - after studying the topic:

Basic concepts, purpose, objectives, principles and methodology of evidence-based medicine;

Degrees of evidence in clinical studies of etiology, diagnosis, treatment and prognosis and the scope of its practical application;

The main historical stages of the formation and development of evidence-based medicine;

The significance of the Cochrane Collaboration for clinical medicine and the forms of its activities abroad and in Russia;

Difficulties in implementing evidence-based medical practice and ways to overcome them

The student must be able to:

- competently and independently analyze and evaluate and analyze the clinical features of the manifestation of the patient's pathology and carry out their activities taking into account the principles and methodology of evidence-based medicine;

Use the information resources of the Cochrane Library to make clinical decisions based on the principles of evidence and reliability in order to obtain a high-quality and effective clinical outcome.

The student must be proficient in:

Terms and concepts clinical epidemiology;

Measuring total error in a clinical trial;

Assessment of health levels in medical and social studies;

Methods for calculating indices and indicators of health;

Formation of a cohort for scientific and clinical research;

Formation of a population for scientific and clinical research.

Tasks for independent extracurricular work of students on the specified topic:

1 - get acquainted with the theoretical material on the topic of the lesson using lecture notes and / or recommended educational literature and sources;

2 - to state in writing in the workbook "Glossary" the essence of the terms and concepts used on this topic of the seminar:

N/N n/n	Term / concept	The essence of the term / concept
	Epidemiology	-
	Clinical epidemiology
	Random error
	systematic error
	total measurement error
	Study
	Trial
	Health
	Disease
	Health resources
	Health Potential
	Health balance
	Risk factors
	Risk factors for poor health
	Cohort
	population
	Organization of the study
	Factor signs
	Effective signs
	Data summary and grouping program
	Study plan
	Data collection
	Continuous epidemiological study
	Selective epidemiological studies
	Study case - control
	cohort study
	observational study
	Pilot study
	randomized clinical controlled trial

Clinical epidemiology and diagnostic tests Pre-test probability of the presence of a disease Sensitivity and specificity of a diagnostic test Predictive value of a diagnostic test Population with a low probability of disease Lecture abstracts: Principles of evidence-based medicine a relatively short period of time, the main ...

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F KSMU 4/3-04/01

IP No. 6 UMS at KazGMA

KARAGANDA STATE MEDICAL UNIVERSITY

Department of Epidemiology and Communal Hygiene

LECTURE

Topic: "Basic provisions and principles of clinical epidemiology, the relationship of clinical epidemiology with biostatistics."

Subject: BDO 26 Epid - 3226 Epidemiology

Specialty: 051301 - " General Medicine »

Course 3

Time (duration) 1 hour

Karaganda 2010

Approved at the meeting of the department

"____" ____________ 2010 Protocol No. ___

Head Department of Epidemiology and

of Communal Hygiene Doctor of Medical Sciences, Professor __________ Shabdarbayeva M.S.

Topic: "Basic provisions and principles of clinical epidemiology, the relationship of clinical epidemiology with biostatistics".

Purpose: mastering the scientific and organizational foundations of clinical epidemiology.

Lecture plan:

Lecture abstracts:

Principles of evidence-based medicine

The term "evidence-based medicine" or "evidence-based medicine" ( evidence based medicine ) appeared in the lexicon of modern medical specialists quite recently, however, in a relatively short period of time, the basic principles invested in the meaning of this term constituted the dominant ideology of medicine. XXI century. With the help of “evidence”, it became possible, if not to make medicine an exact science, then at least to bring it closer to one.

This term was proposed in 1990 by a group of Canadian scientists from McMaster University in Toronto.

The definition formulated by the Evidence-Based Medicine Working Group, with some of our additions, is as follows:

“Evidence-based medicine is a branch of medicine based on evidence, involving the search, comparison and wide dissemination of the evidence obtained for use in the interests of patients (clinical epidemiology) or in the interests of the whole population (preventive evidence-based medicine).”

Recently, there are various options for defining the concept of "evidence-based medicine" (EBM):

DM is a benign, accurate and meaningful use of the best results of clinical trials to select the treatment of a particular patient (clinical epidemiology);
DM is a method (variant) of medical practice, when the doctor uses only those methods in the management of the patient, the usefulness of which has been proven in benign studies (clinical epidemiology);
DM is an approach to health care that collects, interprets and integrates reliable, important and applicable evidence from special studies, taking into account the observations of clinicians and complaints from patients (clinical epidemiology), as well as the state of health of the population (public health) ;
DM is a new approach to technologies for collecting, summarizing and interpreting
medical information.

The essence of the above definitions is to optimize the quality of medical services to the population (a specific patient) in terms of their safety, benefits, efficiency, acceptable cost, etc. -2010" and the strategic direction of the activities of the Ministry of Health of the Republic of Kazakhstan to control the quality of medical and pharmaceutical care for the population.

Evidence-based medicine is based on "clinical epidemiology", which is a branch of medicine that uses epidemiological methods to obtain medical information based only on strictly proven scientific facts, excluding the influence of systematic and random errors.

Term clinical epidemiology(CE) comes from the name of two "parent" disciplines: "clinical medicine" and "epidemiology". It is necessary to clearly distinguish between the purpose and purpose of these two disciplines and the tasks of clinical epidemiology:

"clinical epidemiology" ( clinical epidemiology ) is a "clinical" science because it seeks to answer clinical questions and recommend clinical decisions based on the most reliable evidence. In other words, "clinical epidemiology" is a science that develops clinical research methods that make it possible to draw comprehensively sound conclusions, controlling the influence of systematic and random errors;
from an epidemiological point of view, this is a branch of medicine that uses epidemiological methods to obtain medical information based only on strictly proven scientific facts that are not affected by systematic and random errors. Consequently, epidemiology is a field of science, where its various directions (identification of “risk” factors or a causal factor, or a module of causality, behind which a “consequence” is opened in the form of a disease and the doctor’s response measures - ways to eliminate them) are carried out by an epidemiologist in a wide range of real facts . Here, specific assistance to the patient is considered in the context of a large population of the population (a group of people at risk of disease (infection), to which a specific individual (sick person) belongs);
a close relationship between the epidemiologist and the clinician is necessary, without which their actions are limited, uncoordinated and ineffective in addressing the issue of protecting the health of a particular person and the population as a whole.

The main postulate of clinical epidemiology isany decision in medical practice must be based on rigorously proven facts,which are the basis for evidence-based medicine.

Being a part of medicine, epidemiology as a science differs from clinical medical practice in its approach to the problem: the epidemiologist studies the differences and common features of diseases in order to help large groups of people (population, population). Actually, the “epidemiological diagnosis” differs from the “clinical diagnosis”. In the first case, the causes, conditions and mechanisms of the formation of the incidence of the population are determined by analyzing its distribution across territories, among various groups and collectives, as well as over time and among subjects with different characteristics. At the same time, diseases are separated as a phenomenon observed in an individual organism (clinical epidemiology) and morbidity (a set of cases in a population). In the case of a "clinical diagnosis", the disease is considered in a specific individual. It should be noted that only the elimination of "risk factors" for the occurrence of a disease of an infectious or somatic nature (population morbidity) can solve the main issue - maintaining and improving the health of the population. Therefore, epidemiology is considered to be the foundation of public health science.

In a narrow sense, the task of evidence-based medicine is to transform the results of scientific research into concrete clinical and preventive solutions and recommendations for physicians.

An important aspect of evidence-based medicine has become the establishment of the degree of reliability and significance, i.e. "evidence" of medical information.

According to the Swedish Council for Health Evaluation Methodology, the reliability of evidence from different sources is not uniform and depends on the type of study conducted. Confidence decreases in this order:

randomized controlled clinical trial;
non-randomized clinical trial with simultaneous control;
non-randomized clinical trial with historical control;
cohort study;
"case-control";
cross clinical trial;
observation results.

Meta-analysis

Randomized (extreme) controlled trials (the "gold standard")

Analytical studies (cohort, "case control")

Descriptive studies

Expert opinion

The assessment of the reliability (evidence) of the information received implies the answer to three main questions:

Are the results of the studies justified (validity)?
What are these results (reliability/validity)?
Will the on-site results help (applicability)?

The Center for Evidence-Based Medicine at Oxford offers the following criteria for the reliability of medical information:

High Confidence- information is based on the results of several independent clinical trials with concurrence of results summarized in systematic reviews.

Moderate certainty- the information is based on the results of at least several independent, similar clinical trials.

Limited certainty- the information is based on the results of one clinical trial.

There is no rigorous scientific evidence(clinical trials not conducted) - a certain statement is based on the opinion of experts.

Applied to laboratory diagnosticsevidence must be provided at several levels:

at the technical (or technological) levelit is necessary to prove that the information obtained reliably reflects the state of the function of an organ or tissue of interest to the researcher;
at the diagnostic levelit must be shown that the analysis being performed is in a proven causal relationship with the suspected pathology and the correspondinglaboratory testhas a certaindiagnostic specificity(number of negative responses in the healthy group) andsensitivity(number of positive test responses in a group of patients with a given disease).

For a comprehensive assessment of the test in terms of its sensitivity and specificity, graphs of characteristic curves are used.

At its core, evidence-based medicine is a new approach to the technology of collecting, analyzing, summarizing and interpreting facts and information for the processes of diagnosis, treatment and prevention, the purpose of which is to provide evidence-based criteria and principles for planning, conducting, analyzing clinical, diagnostic, epidemiological studies and application their results in everyday practical medical activity, calledevidence-based medical practice.

Clinical epidemiology and diagnostic tests

The materials of the Oxford Center for Evidence-Based Medicine include the following aspects:

pre-test probability of having a disease;
sensitivity and specificity of the Diagnostic study
(indicators of sensitivity and specificity of some diagnostic
ical tests);
predictive value of a diagnostic test.

Pre-test probability of having a disease

Project assessments of situations prior to receiving the results of a diagnostic test. Pre-test probability is especially useful in four cases:

When interpreting the results of a diagnostic study.
When selecting one or more diagnostic studies.
When choosing whether to start therapy:

A) without further investigation (treatment threshold);

B) while waiting for further research.

When deciding whether to conduct a study at all (testing threshold).

Sensitivity and specificity of the diagnostic test

Any clinical test(lab test, objective test) is not perfect. There is always the possibility that test results do not reflect the objective presence or absence of a disease.

The presence (or absence) of pathology is established by a certain reference, standard method, otherwise called the “gold standard of diagnosis”. It is clear that the reference method is also not 100% accurate. As a rule, the use of the reference diagnostic method is limited by a number of inconveniences - from a high risk of complications to high cost.

To judge how good a given diagnostic test isrelative to the standardthe concepts of sensitivity and specificity of a diagnostic test are proposed.

Sensitivity ( sensitiviny ): the proportion of people with a disease who have a positive diagnostic test.

specificity ): the proportion of people without disease who have a negative diagnostic test.

To illustrate the relationship between the results of a clinical test and an objectively existing (or non-existing) pathology, the so-calledquadruple table.

Building a four-field table
		Disease
		Present	Missing
Test	Positive			a+b
	Negative			c+d
		a+c	b+ d

Sensitivity ( Se) \u003d a / (a + c)

Specificity (S p) = d /(b+ d )

Sensitive Testoften gives a positive result in the presence of the disease (detects it). However, it is especially informative when it gives a negative result, because. rarely misses sick patients.

specific testrarely gives a positive result in the absence of disease. It is especially informative with a positive result, confirming the (presumed) diagnosis.

There are two rules that greatly aid in the use of sensitivity and specificity data for a diagnostic test:

1 rule reminding that a highly sensitive sign, test or symptom, if negative, excludes the disease;
2 rule reminding that a highly specific sign, test or symptom, if positive, confirms the disease.

The predictive value of a diagnostic test

The predictive value of the test is the probability of the presence (absence) of the disease with a known result of the study.

As disease prevalence approaches 0%, the positive predictive value approaches zero.

As the prevalence approaches 100%, the negative predictive value tends to zero.

After conducting a clinical test (not necessarily a laboratory one), it is necessary to answer the main question - is the subject sick. This is where the concept of the predictive value of a test comes in handy.

The predictive value of a positive result is the probability of having a disease in a positive (abnormal) test result.

The predictive value of a negative result is the probability of the absence of disease in a negative (normal) test result.

Factors that determine the predictive value of a test

The predictive value depends on:

sensitivity and specificity of the diagnostic method;
the prevalence of the disease in the study population.

Prevalence (p revalen ce) is defined as the ratio of the number of individuals with a disease (or any other condition) to the entire study population. The prevalence is called a priori (pretest) probability, i.e. is the probability of detecting a disease before the test results are known. The predictive value is called the posterior (post-test) probability of the disease.

The formula that relates the sensitivity, specificity, and prevalence of a disease to the positive predictive value is derived from Bayes' theorem.

where

R V - Positive predictive value

S e - Sensitivity

P - Prevalence

(according to R. Fletcher et al. Clinical epidemiology. Fundamentals of evidence-based medicine, M., 2004)

The more sensitive the negative result (i.e., it increases the likelihood that negative test results reject the presence of the disease). On the contrary, than more specific test, the higher its predictive value positive result (i.e., the likelihood that a positive test result confirms a suspected diagnosis increases).

Interpretation of predictive value

The interpretation of the predictive value of a positive or negative test result varies with the prevalence of the disease.

Population with a low probability of disease

If positive results of even a highly specific test are obtained in a population withlow probabilitydiseases, they will be predominantlyfalse positive.

In a population without the disease being studied, all positive results will be false positives, so as the prevalence of the disease goes to zero, the positive predictive value goes to zero.

Population with a high probability of disease

Negative results of a highly sensitive test obtained in a population with a high probability of having the disease are more likely to be false negatives.

In a population where everyone has the disease, all negative results, even on a highly sensitive test, will be false negatives. As prevalence approaches 100%, the negative predictive value approaches zero.

Illustrated material (tables, slides).

Research Evidence Pyramid
Construction of a four-field table.

Literature:
Vlasov V.V. Epidemiology. Tutorial. 2nd edition M., 2006
Pokrovsky V.I., Briko N.I. Guide to practical exercises in general epidemiology with the basics of evidence-based medicine. Textbook M., 2008.
Yushchuk N.D., Martynov Yu.V. Epidemiology. - M.: Medicine, 2003.
Amireev S.A. Epidemiology. 2 vol. Almaty 2002.

Control questions (feedback):

Principles of evidence-based medicine.
Clinical epidemiology and diagnostic tests.
Pre-test probability of having a disease.
Sensitivity and specificity of the diagnostic test.
The predictive value of a diagnostic test.
Population with a low probability of disease.

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Topic: "Clinical epidemiology: definition, history of development, basic principles and research methods"

Obasic concepts of clinical epidemiology

Historically, in the 20th century in the USSR, ideas about epidemiology as a science were associated primarily with the study of the epidemic process. This is understandable, because revolutions, collectivization and industrialization, two world wars, then the collapse of the USSR more than once led to an economic catastrophe, which was accompanied by a massive spread of infectious diseases. At the same time, science in the USSR was in relative isolation from the world.

In the same historical period, epidemiological analytical studies of the causes of the spread of non-communicable diseases (cardiovascular and oncological diseases, diseases associated with environmental degradation, etc.) were intensively improved in the countries of Western Europe and the United States. Their results have become widely used in clinical medicine. Simultaneously, epidemiological studies of social influences on human health developed. Epidemiology was transformed into a science not about the spread of infectious diseases, but about the spread of diseases and the factors influencing their spread. The object was not the epidemic process, but the process of the spread of diseases. The methodology of clinical research has also deepened. They made it possible to obtain reliable information about the causes of morbidity, about the effectiveness of certain medical interventions.

The DM methodology is based on epidemiology. Currently, from the general epidemiology, clinicalepidemiology(CE), as a science "allowing prediction for each individual patient based on the study of the clinical course of the disease in similar cases using rigorous scientific methods of studying groups of patients to ensure the accuracy of the forecast." It is even called "the science of the methodology of medicine."

The main goal of CE is "the introduction of methods of clinical research and data analysis that ensure the right decision-making", because. any science seeks to know some phenomenon, process or object using an adequate method.

The epidemiological method is a set of techniques designed to study the causes, conditions for the occurrence and spread of diseases and other conditions in a population of people.

In the process of evolution of the epidemiological method, 3 main groups of epidemiological methods were distinguished:

descriptive (descriptive),

analytical,

experimental.

This brief outline of research methodology is not intended to be a study of research methods. Its purpose is to give the reader the knowledge needed to critically read research reports, i.e. for the most important skill for practicing DM.

The main scientific categories in CE are the concepts of random and systematic error, which came to medicine from statistics. Biostatistics - the application of statistical methods in biology and medicine - is an important scientific tool for epidemiological research. Knowledge of its foundations is necessary for the practice of DM, since it operates with quantitative data. Sometimes they try to reduce CE to statistical research methods, but this is erroneous, since statistics, on the one hand, is just a research tool, and on the other hand, it is a completely independent science.

The main task of CE is to apply the principles of clinical research to obtain reliable knowledge and critical evaluation of research results in order to improve medical practice.

The main thing in evaluating the results of a clinical trial is to evaluate its design, which should be adequate to the subject of the study. The quality of the developed design characterizes the methodological maturity of the researcher who plans its implementation. Understanding the types of research designs is essentially understanding the nature of clinical epidemiology.

A key element in the CE approach to clinical research and in the practice of DM is the approach to disease outcomes. The CE draws attention to the fact that in order to evaluate interventions, it is necessary to study their impact on outcomes such as death, discomfort, disability, and patient dissatisfaction. These outcomes are called clinically important or important to patients. Outcomes in the form of changes in concentrations, densities and other features (surrogate outcomes) in DM are considered as having no significant value for practice.

Fleming T.R. and De Mets D.L., who conducted special studies using the results of cohort studies as an example, showed that in various diseases, the use of surrogate outcomes as criteria for the effectiveness of treatment can lead to erroneous conclusions compared with the clinical outcomes that have occurred.

It must be remembered that DM technologies cannot and should not completely replace the old principles of clinical practice, they only supplement them and offer new, more effective solutions. From these positions, it is of interest to analyze the state of application of DM technologies in developed countries. It shows that real clinical decisions are made under the influence of a number of factors, such as the characteristics of a medical institution, the level of training of a doctor, patient preferences, etc. At the same time, the main principle of making a clinical decision is the choice of a patient with full information of the latter. This principle is confirmed by the Sicilian declaration on the use of DM technologies, which was approved on 01/05/2005.

CE is relatively difficult to study. However, without knowledge of its fundamentals, a modern specialist cannot assess the quality of a scientific publication, navigate modern information, determine the price of a decision (risk / benefit ratio), the reliability of the study, and critically evaluate clinical recommendations. As a result, a doctor who is not oriented in CE cannot methodically correctly apply the results of scientific research to a particular patient.

In his daily activities, the doctor solves the problem of a particular patient, and at the same time, the task facing the doctor and his practical experience determine the choice of an answer to a clinical question. He knows all his patients by sight, collects anamnesis, conducts research and is personally responsible for each patient. As a result, the doctor evaluates, first of all, the individual characteristics of each patient, and with great reluctance he combines his patients into groups according to risk, diagnosis, method of treatment and evaluates the patient's belonging to these groups in terms of probability theory.

Figure 1. Three main components of evidence-based medicine.

The physician's personal experience is also important for clinical decision making. However, the vast majority of doctors do not have sufficient practical experience to recognize all the subtle, long-term, interacting processes that take place in most chronic diseases.

The object of study of clinical epidemiology is the medical aspects of diseases. For example, how are symptom and disease, intervention and outcome related. To assess how much research results can be trusted, the clinician must understand how medical research should be conducted.

Thus, the physician, in order to judge the reliability of clinical information, needs to know the basic concepts of clinical epidemiology, as well as anatomy, pathology, biochemistry, and pharmacology. Therefore, at present, clinical epidemiology is considered as one of the fundamental sciences on which the building of modern medicine rests.

ClinicalepidemiologyandsocialAspectsmedicalhelp

clinical epidemiology population assistance

In connection with the introduction of the achievements of modern science, new technologies and drugs into practical medicine, the cost of medical care has reached such a level that even the richest groups of the population are not able to pay for all the desired types of services. At the same time, the use of new types of medical interventions is not always accompanied by a proportional improvement in clinical outcomes. As a result, methods are being developed for a more thorough, generalized assessment of scientific clinical evidence that health care leaders can use to improve health care delivery.

Today, few people dispute the position that medical care should be based on the results of properly conducted research and evaluated by the final results, taking into account the financial costs that society can afford. Also, each patient is considered as an integral part of large groups of similar patients, which helps not only to make more accurate individual predictions, but also to choose the best way to use limited financial resources to improve care for the largest possible contingent of people.

Mainprovisionsandprinciplesclinicalepidemiology

The main goal of CE is to introduce clinical research methods that ensure that the right decisions are made. In this case, of course, personal experience and knowledge of the mechanisms of disease development are important. However, other important aspects must be taken into account.

In most cases, the diagnosis, prognosis and treatment results for a particular patient are not determined with accuracy and therefore must be expressed in terms of probabilities.

The probabilities for a particular patient are best determined on the basis of previous experience gained from a similar group of patients.

It should always be taken into account that clinical observations should be carried out on patients who are free in their behavior, who are observed by doctors with different qualifications and their own opinions, which can lead to systematic errors leading to erroneous conclusions.

Any clinical study is subject to randomness and the result of each study can be distorted by random error.

To reduce errors in decision-making, the clinician should use the results of studies based on rigorous scientific principles, using methods to minimize systematic errors and take into account possible random errors.

Clinical questions and answers to them are based on the principles and concepts given below.

Clinicalquestions

The main questions that clinical epidemiology poses are: abnormalities, diagnosis, frequency, risk, prognosis, treatment, prevention, cause, costs. These are the questions that arise for both the patient and the doctor. These are the most commonly discussed between doctors and patients.

Clinicaloutcomes

For CE, the most interesting outcomes are those that are of vital importance for patients, as well as medical personnel - death, illness, discomfort, disability, dissatisfaction with treatment. It is these phenomena that doctors want to understand, predict, interpret and change in the treatment of patients.

CE differs from other medical sciences in that all these phenomena are studied directly on humans, and not on experimental animals or elements of the human body, such as tissue cultures, cell membranes, receptors and mediators, nucleic acid sequences, etc. Biological events cannot be considered equivalent to clinical outcomes until there is direct evidence of their relationship.

Quantitative approach

Correct measurements should be used in benign clinical trials, as less reliable measurements provide less reliable evidence. The frequency and severity of clinical outcomes, such as death, disease, or disability, can be expressed numerically. Functional defect and loss of quality of life can also be measured. In benign studies, the unreliability of human subjective assessments must be taken into account, and a correction must be made for this unreliability.

It is very rare to predict clinical outcome with high accuracy. Most often, based on the results of previous studies on similar patients, the probability of a particular outcome is determined. The clinico-epidemiological approach assumes that clinical prognosis is uncertain, but can be quantified as probabilities. For example, symptoms of coronary heart disease occur in 1 in 100 middle-aged men per year; Smoking doubles the risk of death at any age.

Populationsandsamples

A population is a large group of people living in a certain geographical region (for example, in Kazakhstan) and reproducing itself in a number of generations. This is a general biological definition of a population; as applied to a person, it is a synonym for population. In epidemiology and in the clinic, a population is also called any group of people who have some common feature (for example, people over 65 years old, or hotel workers). A population may represent only a subset of the population (eg, in epidemiological studies of the causes of disease). It may consist of patients admitted to a particular clinic or patients with a particular disease (which is more common in clinical trials). Therefore, one can speak of the general population, the hospital population, or the population of patients with a specific disease.

A sample is a specially selected part of a population. Clinical studies are usually performed on samples because it is not possible and usually not necessary to study the entire population. In order for the sample to reflect the population correctly (be representative, i.e. representative), it must be correctly created. In the simplest case, this is a random sample from a population. In fact, for various reasons, it is not always easy to randomly select members of a population, so more or less complex (compared to a simple sample) techniques are used. In addition, the sample must be large enough so that the estimates obtained from it, for example, the frequency of events, are sufficiently accurate. It is advisable to determine the required sample size before starting research using standard statistical formulas.

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